Instructions to use xfcghj/AR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xfcghj/AR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xfcghj/AR", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 375 Bytes
f0fc238 394918c f0fc238 394918c f0fc238 394918c f0fc238 394918c f0fc238 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | #!/bin/bash
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
torchrun \
--nproc_per_node=8 \
--master_port=29898 \
train.py \
--num_refine_groups 3 \
--batch_size 6 \
--lr 1e-4 \
--epochs 100 \
--save_every_k_epochs 1 \
--last_ckpt /home/dataset-assist-0/usr/lh/ysh/dw/RL/AR/checkpoints/20260617_190742/model_step_4500.pth \
--auto_resume |